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DTSTAMP:20240214T070311Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T090000
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UID:siggraphasia_SIGGRAPH Asia 2023_sess124@linklings.com
SUMMARY:Avatar Portrait
DESCRIPTION:Technical Papers, TOG\n\nAvatarStudio: Text-driven Editing of 
 3D Dynamic Human Head Avatars\n\nCapturing and editing full head performan
 ces enables the creation of virtual characters with various applications s
 uch as extended reality and media production. The past few years witnessed
  a steep rise in the photorealism of human head avatars. Such avatars can 
 be controlled through different input...\n\n\nMohit Mendiratta, Xingang Pa
 n, Mohamed Elgharib, Kartik Teotia, and Mallikarjun B R (Max Planck Instit
 ute for Informatics); Ayush Tewari (MIT CSAIL); Vladislav Golyanik (Max Pl
 anck Institute for Informatics); Adam Kortylewski (Max Planck Institute fo
 r Informatics, University of Freiburg); and Christian Theobalt (Max Planck
  Institute for Informatics)\n---------------------\nNeural Point-based Vol
 umetric Avatar: Surface-guided Neural Points for Efficient and Photorealis
 tic Volumetric Head Avatar\n\nRendering photo-realistic and vividly moving
  human heads is very important for pleasant and immersive experience in AR
 /VR and video conferencing. However, existing methods usually struggle to 
 model challenging facial regions (e.g., mouth interior, eyes, hair/beard),
  resulting in unrealistic and blur...\n\n\nCong Wang (Tsinghua University)
 ; Di Kang, Yan-Pei Cao, Linchao Bao, and Ying Shan (Tencent); and Song-Hai
  Zhang (Tsinghua University)\n---------------------\nBakedAvatar: Baking N
 eural Fields for Real-Time Head Avatar Synthesis\n\nSynthesizing photoreal
 istic 4D human head avatars from videos is essential for VR/AR, telepresen
 ce, and video game applications. Although existing Neural Radiance Fields 
 (NeRF)-based methods achieve high-fidelity results, the computational expe
 nse limits their use in real-time applications. To overc...\n\n\nHao-Bin D
 uan (Beihang University); Miao Wang (Beihang University, Zhongguancun Labo
 ratory); Jin-Chuan Shi and Xu-Chuan Chen (Beihang University); and Yan-Pei
  Cao (Tencent)\n---------------------\nNeRFFaceLighting: Implicit and Dise
 ntangled Face Lighting Representation Leveraging Generative Prior in Neura
 l Radiance Fields\n\n3D-aware portrait lighting control is an emerging and
  promising domain, thanks to the recent advance of generative adversarial 
 networks and neural radiance fields. Existing solutions typically try to d
 ecouple the lighting from the geometry and appearance for disentangled con
 trol with an explicit lig...\n\n\nKaiwen Jiang (Institute of Computing Tec
 hnology, Chinese Academy of Sciences; Beijing Jiaotong University); Shu-Yu
  Chen (Institute of Computing Technology, Chinese Academy of Sciences); Ho
 ngbo Fu (School of Creative Media, City University of Hong Kong); and Lin 
 Gao (Institute of Computing Technology, Chinese Academy of Sciences; Unive
 rsity of Chinese Academy of Sciences)\n---------------------\nAniPortraitG
 AN: Animatable 3D Portrait Generation from 2D Image Collections\n\nPreviou
 s animatable 3D-aware GANs for human generation have primarily focused on 
 either the human head or full body. However, head-only videos are relative
 ly uncommon in real life, and full body generation typically does not deal
  with facial expression control and still has challenges in generating ...
 \n\n\nYue Wu (Hong Kong University of Science and Technology), Sicheng Xu 
 (Microsoft Research Asia), Jianfeng Xiang (Tsinghua University), Fangyun W
 ei (Microsoft Research Asia), Qifeng Chen (Hong Kong University of Science
  and Technology), and Jiaolong Yang and Xin Tong (Microsoft Research Asia)
 \n\nRegistration Category: Full Access\n\nSession Chair: Lin Gao (Universi
 ty of Chinese Academy of Sciences)
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